Secure Point Cloud Streaming with Learning-Based Super-Resolution

Abstract

Immersive formats such as 360° and 6DoF point cloud videos require high bandwidth and low latency, posing challenges for real-time AR/VR streaming. This work focuses on reducing bandwidth consumption and encryption/decryption delay, two key contributors to overall latency. We design a system that downsamples point cloud content at the origin server and applies partial encryption. At the client, the content is decrypted and upscaled using an ML-based super-resolution model. We integrate this pipeline into an adaptive bitrate (ABR) streaming client, which enables the system to dynamically select among multiple resolution levels and apply Super-Resolution (SR) to enhance delivered quality. We evaluate our approach through a full streaming deployment on CloudLab where we compare SR-aided ABR streaming against a standard ABR baseline under varying cache configurations. Our results show that the SR-aided approach raises the average delivered resolution from approximately 18% to 31% of full density under constrained network conditions, a 76% relative improvement over the ABR-only baseline. Additionally, we demonstrate a nearly linear reduction in bandwidth consumption and encryption/decryption overhead with lower downsampling resolutions while maintaining the ability to effectively reconstruct the original full-resolution point clouds with inference times as low as 68 ms for a 157K-point frame.